Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement.

ABSTRACT: Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditional electromagnetic materials, such analog processors have been so far largely confined to simple linear projections such as image edge detection or matrix multiplications. Complex neuromorphic computing tasks, which inherently require strong non-linearities, have so far remained out-of-reach of wave-based solutions, with a few attempts that implemented non-linearities on the digital front, or used weak and inflexible non-linear sensors, restraining the learning performance. Here, we tackle this issue by demonstrating the relevance of time-

SUBMITTER: Momeni A 

PROVIDER: S-EPMC9098897 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature